TABLE OF CONTENTS A multi-agent system consists of many individual computational agents, distributed throughout an environment, capable of learning environmental management strategies, environmental interaction and inter-agent communication. Multi-agent controllers offer attractive features for the optimisation of many real world systems. One such feature is that agents can operate in isolation, or in parallel with other agents and traditional control systems. This allows multi-agent controllers to be implemented incrementally as funds allow. The distrusted nature of multi-agent control allows local control to be integrated into a global system. Each of the individual agent controllers can be comparatively simple and optimised for local problems. Through agent communication, the agents can take into consideration the global goals of a system when they are developing their local behaviour strategies. Multi-agent controllers have adaptive behaviour, developed by the artificial intelligence community, to learn and optimise local behaviour and at the same time contribute to the global system performance.
Many real world problems are both non-deterministic and non-stationary in nature. In complex environments, there may be many variables that need to be considered, making classical analytic methods difficult to implement. Multi-agent techniques allow complex problems to be deconstructed into simpler problems, and harness expert knowledge about subproblems to develop local solutions.
There are many different flavours of intelligent behaviour developed by the artificial intelligence community. Expert systems were among the earliest solutions. These use the knowledge of domain experts to construct deterministic behaviours based on...
